{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize the `rayleigh_taylor_instability` dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import glob\n",
    "\n",
    "import h5py\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['data/train/rayleigh_taylor_instability_At_0625.hdf5', 'data/train/rayleigh_taylor_instability_At_125.hdf5', 'data/train/rayleigh_taylor_instability_At_25.hdf5', 'data/train/rayleigh_taylor_instability_At_50.hdf5', 'data/train/rayleigh_taylor_instability_At_75.hdf5']\n"
     ]
    }
   ],
   "source": [
    "# print the list of paths of files in the training set\n",
    "set_path = \"train\"\n",
    "paths = sorted(glob.glob(f\"data/{set_path}/*.hdf5\"))\n",
    "print(paths)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<KeysViewHDF5 ['boundary_conditions', 'dimensions', 'scalars', 't0_fields', 't1_fields', 't2_fields']>\n"
     ]
    }
   ],
   "source": [
    "# select the first path (arbitrary choice)\n",
    "p = paths[0]\n",
    "\n",
    "# print the first layer of keys\n",
    "with h5py.File(p, \"r\") as f:\n",
    "    print(f.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "print bc available: <KeysViewHDF5 ['x_periodic', 'y_periodic', 'z_wall']>\n",
      "print attributes of the bc: <KeysViewHDF5 ['associated_dims', 'associated_fields', 'bc_type', 'sample_varying', 'time_varying']>\n",
      "get the bc type: PERIODIC\n"
     ]
    }
   ],
   "source": [
    "# In 'boundary_conditions' is stored the information about the boundary conditions:\n",
    "with h5py.File(p, \"r\") as f:\n",
    "    print(\"print bc available:\", f[\"boundary_conditions\"].keys())\n",
    "    print(\"print attributes of the bc:\", f[\"boundary_conditions\"][\"x_periodic\"].attrs.keys())\n",
    "    print(\"get the bc type:\", f[\"boundary_conditions\"][\"x_periodic\"].attrs[\"bc_type\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t0_fields: <KeysViewHDF5 ['density']>\n",
      "t1_fields: <KeysViewHDF5 ['velocity']>\n",
      "t2_fields: <KeysViewHDF5 []>\n"
     ]
    }
   ],
   "source": [
    "# Reminder: 't0_fields', 't1_fields', 't2_fields' are respectively scalar fields, vector fields and tensor fields\n",
    "# print the different fields available in the dataset\n",
    "with h5py.File(p, \"r\") as f:\n",
    "    print(\"t0_fields:\", f[\"t0_fields\"].keys())\n",
    "    print(\"t1_fields:\", f[\"t1_fields\"].keys())\n",
    "    print(\"t2_fields:\", f[\"t2_fields\"].keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape of the selected t0_field:  (9, 119, 128, 128, 128)\n"
     ]
    }
   ],
   "source": [
    "# The data is of shape (n_trajectories, n_timesteps, x, y)\n",
    "# Get the first t0_field and save it as a numpy array\n",
    "with h5py.File(p, \"r\") as f:\n",
    "    density = f[\"t0_fields\"][\"density\"][:] # HDF5 datasets can be sliced like a numpy array\n",
    "    print(\"shape of the selected t0_field: \", density.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "traj = 1  # select the trajectory\n",
    "x_slice = 64  # select the x slice\n",
    "traj_toplot = density[traj, :, x_slice, ...]\n",
    "# field is now of shape (n_timesteps, x, y). Let's do a subplot to plot it at t= 0, t= T/3, t= 2T/3 and t= T:\n",
    "fig, axs = plt.subplots(1, 4, figsize=(20, 5))\n",
    "T = traj_toplot.shape[0]\n",
    "\n",
    "# fix colorbar for all subplots:\n",
    "normalize_plots = True\n",
    "cmap = \"RdBu_r\"\n",
    "\n",
    "if normalize_plots:\n",
    "    vmin = np.min(traj_toplot)\n",
    "    vmax = np.max(traj_toplot)\n",
    "    norm = plt.Normalize(vmin=vmin, vmax=vmax)\n",
    "    for i, t in enumerate([0, T // 3, (2 * T) // 3, T - 1]):\n",
    "        axs[i].imshow(traj_toplot[t].T, cmap=cmap, norm=norm, origin=\"lower\")\n",
    "        axs[i].set_xticks([])\n",
    "        axs[i].set_yticks([])\n",
    "        axs[i].set_title(f\"t={t}\")\n",
    "else:\n",
    "    for i, t in enumerate([0, T // 3, (2 * T) // 3, T - 1]):\n",
    "        axs[i].imshow(traj_toplot[t].T, cmap=cmap, origin=\"lower\")\n",
    "        axs[i].set_xticks([])\n",
    "        axs[i].set_yticks([])\n",
    "        axs[i].set_title(f\"t={t}\")\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "MLtest",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
